A Quantitative Study for Developing a Computerized System for Bone Age Assessment in University of Malaya Medical Center

Mansourvar, Marjan; Ismail, Maizatul Akmar; Kareem, Sameem Abdul; Nasaruddin, Fariza Hanum; Raj, Ram Gopal

doi:10.1007/978-981-4585-18-7_74

Cited by 3 publications

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“…The results from both these assessment types are associated with human observation variability, since a radiologist doing a bone age assessment to evaluate a child’s maturation cannot be certain about estimation accuracy [ 7 , 8 ]. Therefore, this has been the greatest motivator for presenting an automated method of estimating skeletal maturity (bone age)[ 9 ]. However, the computerized BAA system is still under the empirical period because of the inadequate performance of the system [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

et al. 2015

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Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

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Section: Introductionmentioning

confidence: 99%